85,273 research outputs found
Learning in a Landscape: Simulation-building as Reflexive Intervention
This article makes a dual contribution to scholarship in science and
technology studies (STS) on simulation-building. It both documents a specific
simulation-building project, and demonstrates a concrete contribution to
interdisciplinary work of STS insights. The article analyses the struggles that
arise in the course of determining what counts as theory, as model and even as
a simulation. Such debates are especially decisive when working across
disciplinary boundaries, and their resolution is an important part of the work
involved in building simulations. In particular, we show how ontological
arguments about the value of simulations tend to determine the direction of
simulation-building. This dynamic makes it difficult to maintain an interest in
the heterogeneity of simulations and a view of simulations as unfolding
scientific objects. As an outcome of our analysis of the process and
reflections about interdisciplinary work around simulations, we propose a
chart, as a tool to facilitate discussions about simulations. This chart can be
a means to create common ground among actors in a simulation-building project,
and a support for discussions that address other features of simulations
besides their ontological status. Rather than foregrounding the chart's
classificatory potential, we stress its (past and potential) role in discussing
and reflecting on simulation-building as interdisciplinary endeavor. This chart
is a concrete instance of the kinds of contributions that STS can make to
better, more reflexive practice of simulation-building.Comment: 37 page
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Piloting mixed reality in ICT networking to visualize complex theoretical multi-step problems
This paper presents insights from the implementation of a mixed reality intervention using 3d printed physical objects and a mobile augmented reality application in an ICT networking classroom. The intervention aims to assist student understanding of complex theoretical multi-step problems without a corresponding real world physical analog model. This is important because these concepts are difficult to conceptualise without a corresponding mental model. The simulation works by using physical models to represent networking equipment and allows learners to build a network that can then be simulated using a mobile app to observe underlying packet traversal and routing theory between the different devices as data travels from the source to the destination. Outcomes from usability testing show great student interest in the intervention and a feeling that it helped with clarity, but also demonstrated the need to scaffold the use of the intervention for students rather than providing a freeform experience in the classroom
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